Profiling Report

Dataset statistics

Number of variables	22
Number of observations	1440
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	258.8 KiB
Average record size in memory	184.0 B

Variable types

Numeric	17
DateTime	1
Categorical	4

Alerts

`id` is highly overall correlated with `fuel_type_electric` and 2 other fields	High correlation
`fuel_type_petrol` is highly overall correlated with `fuel_type_diesel` and 10 other fields	High correlation
`fuel_type_diesel` is highly overall correlated with `fuel_type_petrol` and 10 other fields	High correlation
`fuel_type_electric` is highly overall correlated with `id` and 3 other fields	High correlation
`fuel_type_others` is highly overall correlated with `fuel_type_electric` and 5 other fields	High correlation
`vehicleClass_MotorCycle` is highly overall correlated with `fuel_type_petrol` and 10 other fields	High correlation
`vehicleClass_MotorCar` is highly overall correlated with `fuel_type_petrol` and 12 other fields	High correlation
`vehicleClass_others` is highly overall correlated with `fuel_type_petrol` and 10 other fields	High correlation
`seatCapacity_1_to_3` is highly overall correlated with `fuel_type_petrol` and 10 other fields	High correlation
`seatCapacity_4_to_6` is highly overall correlated with `fuel_type_petrol` and 11 other fields	High correlation
`seatCapacity_above_6` is highly overall correlated with `fuel_type_petrol` and 11 other fields	High correlation
`brand_new_vehicles` is highly overall correlated with `fuel_type_petrol` and 10 other fields	High correlation
`pre_owned_vehicles` is highly overall correlated with `fuel_type_petrol` and 12 other fields	High correlation
`category_non_transport` is highly overall correlated with `fuel_type_petrol` and 10 other fields	High correlation
`category_transport` is highly overall correlated with `fuel_type_petrol` and 11 other fields	High correlation
`mmm` is highly overall correlated with `quarter`	High correlation
`quarter` is highly overall correlated with `id` and 1 other fields	High correlation
`fiscal_year` is highly overall correlated with `id`	High correlation
`id` is uniformly distributed	Uniform
`district` is uniformly distributed	Uniform
`mmm` is uniformly distributed	Uniform
`quarter` is uniformly distributed	Uniform
`fiscal_year` is uniformly distributed	Uniform
`id` has unique values	Unique
`fuel_type_electric` has 344 (23.9%) zeros	Zeros
`fuel_type_others` has 330 (22.9%) zeros	Zeros
`vehicleClass_MotorCar` has 27 (1.9%) zeros	Zeros
`vehicleClass_AutoRickshaw` has 94 (6.5%) zeros	Zeros
`vehicleClass_Agriculture` has 25 (1.7%) zeros	Zeros
`seatCapacity_4_to_6` has 25 (1.7%) zeros	Zeros
`seatCapacity_above_6` has 28 (1.9%) zeros	Zeros
`pre_owned_vehicles` has 24 (1.7%) zeros	Zeros

Reproduction

Analysis started	2023-09-16 01:21:48.229983
Analysis finished	2023-09-16 01:22:39.493829
Duration	51.26 seconds
Software version	ydata-profiling vv4.2.0
Download configuration	config.json

id
Real number (ℝ)

HIGH CORRELATION UNIFORM UNIQUE

Distinct	1440
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	720.5

Minimum	1
Maximum	1440
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	22.5 KiB

Quantile statistics

Minimum	1
5-th percentile	72.95
Q1	360.75
median	720.5
Q3	1080.25
95-th percentile	1368.05
Maximum	1440
Range	1439
Interquartile range (IQR)	719.5

Descriptive statistics

Standard deviation	415.83651
Coefficient of variation (CV)	0.5771499
Kurtosis	-1.2
Mean	720.5
Median Absolute Deviation (MAD)	360
Skewness	0
Sum	1037520
Variance	172920
Monotonicity	Strictly increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	1	0.1%
968	1	0.1%
966	1	0.1%
965	1	0.1%
964	1	0.1%
963	1	0.1%
962	1	0.1%
961	1	0.1%
960	1	0.1%
959	1	0.1%
Other values (1430)	1430	99.3%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	1	0.1%
2	1	0.1%
3	1	0.1%
4	1	0.1%
5	1	0.1%
6	1	0.1%
7	1	0.1%
8	1	0.1%
9	1	0.1%
10	1	0.1%

Value	Count	Frequency (%)
1440	1	0.1%
1439	1	0.1%
1438	1	0.1%
1437	1	0.1%
1436	1	0.1%
1435	1	0.1%
1434	1	0.1%
1433	1	0.1%
1432	1	0.1%
1431	1	0.1%

month
Date

Distinct	48
Distinct (%)	3.3%
Missing	0
Missing (%)	0.0%
Memory size	22.5 KiB

Minimum	2019-04-01 00:00:00
Maximum	2023-03-01 00:00:00

Histogram

Histogram with fixed size bins (bins=48)

fuel_type_petrol
Real number (ℝ)

Distinct	1251
Distinct (%)	86.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3835.6007

Minimum	0
Maximum	39689
Zeros	10
Zeros (%)	0.7%
Negative	0
Negative (%)	0.0%
Memory size	22.5 KiB

Quantile statistics

Minimum	0
5-th percentile	643.05
Q1	1346.25
median	2067.5
Q3	3412
95-th percentile	18272.45
Maximum	39689
Range	39689
Interquartile range (IQR)	2065.75

Descriptive statistics

Standard deviation	5245.7898
Coefficient of variation (CV)	1.367658
Kurtosis	8.019446
Mean	3835.6007
Median Absolute Deviation (MAD)	918
Skewness	2.8652969
Sum	5523265
Variance	27518311
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	10	0.7%
4	5	0.3%
1260	4	0.3%
1668	3	0.2%
452	3	0.2%
1772	3	0.2%
2267	3	0.2%
1196	3	0.2%
1137	3	0.2%
1021	3	0.2%
Other values (1241)	1400	97.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	10	0.7%
2	2	0.1%
3	1	0.1%
4	5	0.3%
6	2	0.1%
8	2	0.1%
12	1	0.1%
14	1	0.1%
20	1	0.1%
31	1	0.1%

Value	Count	Frequency (%)
39689	1	0.1%
32104	1	0.1%
31646	1	0.1%
28578	1	0.1%
26996	1	0.1%
26643	1	0.1%
26435	1	0.1%
26280	1	0.1%
26093	1	0.1%
25950	1	0.1%

fuel_type_diesel
Real number (ℝ)

Distinct	850
Distinct (%)	59.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	659.29444

Minimum	0
Maximum	5270
Zeros	1
Zeros (%)	0.1%
Negative	0
Negative (%)	0.0%
Memory size	22.5 KiB

Quantile statistics

Minimum	0
5-th percentile	132.9
Q1	261.75
median	445.5
Q3	744
95-th percentile	2234.1
Maximum	5270
Range	5270
Interquartile range (IQR)	482.25

Descriptive statistics

Standard deviation	684.8239
Coefficient of variation (CV)	1.0387224
Kurtosis	8.4759592
Mean	659.29444
Median Absolute Deviation (MAD)	208.5
Skewness	2.6851802
Sum	949384
Variance	468983.78
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
280	7	0.5%
155	6	0.4%
268	5	0.3%
518	5	0.3%
253	5	0.3%
549	5	0.3%
190	5	0.3%
163	5	0.3%
121	5	0.3%
249	5	0.3%
Other values (840)	1387	96.3%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	1	0.1%
6	1	0.1%
15	2	0.1%
19	1	0.1%
25	1	0.1%
30	2	0.1%
32	1	0.1%
34	1	0.1%
35	2	0.1%
36	2	0.1%

Value	Count	Frequency (%)
5270	1	0.1%
4665	1	0.1%
4576	1	0.1%
4336	1	0.1%
4130	1	0.1%
4059	1	0.1%
3961	1	0.1%
3842	1	0.1%
3728	1	0.1%
3695	1	0.1%

fuel_type_electric
Real number (ℝ)

HIGH CORRELATION ZEROS

Distinct	221
Distinct (%)	15.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	71.985417

Minimum	0
Maximum	2782
Zeros	344
Zeros (%)	23.9%
Negative	0
Negative (%)	0.0%
Memory size	22.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	2
median	8
Q3	35
95-th percentile	303.1
Maximum	2782
Range	2782
Interquartile range (IQR)	33

Descriptive statistics

Standard deviation	247.5996
Coefficient of variation (CV)	3.43958
Kurtosis	42.403048
Mean	71.985417
Median Absolute Deviation (MAD)	8
Skewness	5.9992088
Sum	103659
Variance	61305.561
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	344	23.9%
2	159	11.0%
4	85	5.9%
6	65	4.5%
8	44	3.1%
12	26	1.8%
10	24	1.7%
16	21	1.5%
14	18	1.2%
20	18	1.2%
Other values (211)	636	44.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	344	23.9%
1	12	0.8%
2	159	11.0%
3	11	0.8%
4	85	5.9%
5	11	0.8%
6	65	4.5%
7	16	1.1%
8	44	3.1%
9	12	0.8%

Value	Count	Frequency (%)
2782	1	0.1%
2606	1	0.1%
2304	1	0.1%
2237	1	0.1%
2144	1	0.1%
2003	1	0.1%
1628	1	0.1%
1605	1	0.1%
1599	1	0.1%
1582	1	0.1%

fuel_type_others
Real number (ℝ)

HIGH CORRELATION ZEROS

Distinct	243
Distinct (%)	16.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	62.13125

Minimum	0
Maximum	2326
Zeros	330
Zeros (%)	22.9%
Negative	0
Negative (%)	0.0%
Memory size	22.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	2
median	6
Q3	34
95-th percentile	252.05
Maximum	2326
Range	2326
Interquartile range (IQR)	32

Descriptive statistics

Standard deviation	204.99126
Coefficient of variation (CV)	3.2993262
Kurtosis	49.316748
Mean	62.13125
Median Absolute Deviation (MAD)	6
Skewness	6.5038766
Sum	89469
Variance	42021.418
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	330	22.9%
2	177	12.3%
4	100	6.9%
6	67	4.7%
8	47	3.3%
10	43	3.0%
12	23	1.6%
14	22	1.5%
5	21	1.5%
16	20	1.4%
Other values (233)	590	41.0%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	330	22.9%
1	20	1.4%
2	177	12.3%
3	15	1.0%
4	100	6.9%
5	21	1.5%
6	67	4.7%
7	14	1.0%
8	47	3.3%
9	16	1.1%

Value	Count	Frequency (%)
2326	1	0.1%
2086	1	0.1%
2042	1	0.1%
1848	1	0.1%
1755	1	0.1%
1696	1	0.1%
1652	1	0.1%
1636	1	0.1%
1547	1	0.1%
1540	1	0.1%

vehicleClass_MotorCycle
Real number (ℝ)

Distinct	1203
Distinct (%)	83.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3340.2688

Minimum	0
Maximum	35420
Zeros	10
Zeros (%)	0.7%
Negative	0
Negative (%)	0.0%
Memory size	22.5 KiB

Quantile statistics

Minimum	0
5-th percentile	540.45
Q1	1229.75
median	1887
Q3	3149.25
95-th percentile	15030.55
Maximum	35420
Range	35420
Interquartile range (IQR)	1919.5

Descriptive statistics

Standard deviation	4349.4062
Coefficient of variation (CV)	1.3021126
Kurtosis	9.240943
Mean	3340.2688
Median Absolute Deviation (MAD)	822
Skewness	2.9655482
Sum	4809987
Variance	18917334
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	10	0.7%
965	5	0.3%
2	4	0.3%
1223	4	0.3%
1707	4	0.3%
2632	4	0.3%
1532	4	0.3%
2308	3	0.2%
1103	3	0.2%
1555	3	0.2%
Other values (1193)	1396	96.9%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	10	0.7%
2	4	0.3%
3	1	0.1%
4	3	0.2%
6	2	0.1%
8	2	0.1%
12	1	0.1%
14	1	0.1%
20	1	0.1%
31	1	0.1%

Value	Count	Frequency (%)
35420	1	0.1%
27869	1	0.1%
25563	1	0.1%
25527	1	0.1%
24431	1	0.1%
24328	1	0.1%
24285	1	0.1%
22699	1	0.1%
22585	1	0.1%
22499	1	0.1%

vehicleClass_MotorCar
Real number (ℝ)

HIGH CORRELATION ZEROS

Distinct	676
Distinct (%)	46.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	719.74306

Minimum	0
Maximum	8036
Zeros	27
Zeros (%)	1.9%
Negative	0
Negative (%)	0.0%
Memory size	22.5 KiB

Quantile statistics

Minimum	0
5-th percentile	48
Q1	123
median	228.5
Q3	432.5
95-th percentile	4650.85
Maximum	8036
Range	8036
Interquartile range (IQR)	309.5

Descriptive statistics

Standard deviation	1455.4873
Coefficient of variation (CV)	2.0222318
Kurtosis	8.5822349
Mean	719.74306
Median Absolute Deviation (MAD)	126
Skewness	3.0599784
Sum	1036430
Variance	2118443.2
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	27	1.9%
113	10	0.7%
106	9	0.6%
130	8	0.6%
99	8	0.6%
52	7	0.5%
84	7	0.5%
123	7	0.5%
231	7	0.5%
140	7	0.5%
Other values (666)	1343	93.3%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	27	1.9%
2	3	0.2%
16	1	0.1%
17	1	0.1%
20	3	0.2%
21	1	0.1%
22	2	0.1%
27	3	0.2%
30	3	0.2%
32	1	0.1%

Value	Count	Frequency (%)
8036	1	0.1%
7811	1	0.1%
7780	1	0.1%
7696	1	0.1%
7617	1	0.1%
7607	1	0.1%
7492	1	0.1%
7447	1	0.1%
7383	1	0.1%
7359	1	0.1%

vehicleClass_AutoRickshaw
Real number (ℝ)

Distinct	303
Distinct (%)	21.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	93.850694

Minimum	0
Maximum	2287
Zeros	94
Zeros (%)	6.5%
Negative	0
Negative (%)	0.0%
Memory size	22.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	16
median	36
Q3	90
95-th percentile	331.4
Maximum	2287
Range	2287
Interquartile range (IQR)	74

Descriptive statistics

Standard deviation	197.17876
Coefficient of variation (CV)	2.1009835
Kurtosis	44.843973
Mean	93.850694
Median Absolute Deviation (MAD)	28
Skewness	5.9307487
Sum	135145
Variance	38879.463
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	94	6.5%
8	37	2.6%
22	31	2.2%
10	31	2.2%
16	29	2.0%
12	29	2.0%
6	29	2.0%
20	28	1.9%
4	21	1.5%
32	21	1.5%
Other values (293)	1090	75.7%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	94	6.5%
1	8	0.6%
2	18	1.2%
3	3	0.2%
4	21	1.5%
5	5	0.3%
6	29	2.0%
7	10	0.7%
8	37	2.6%
9	9	0.6%

Value	Count	Frequency (%)
2287	1	0.1%
2041	1	0.1%
2006	1	0.1%
1705	1	0.1%
1684	1	0.1%
1640	1	0.1%
1622	1	0.1%
1614	1	0.1%
1518	1	0.1%
1509	1	0.1%

vehicleClass_Agriculture
Real number (ℝ)

Distinct	381
Distinct (%)	26.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	135.9125

Minimum	0
Maximum	786
Zeros	25
Zeros (%)	1.7%
Negative	0
Negative (%)	0.0%
Memory size	22.5 KiB

Quantile statistics

Minimum	0
5-th percentile	6.95
Q1	49
median	105
Q3	189
95-th percentile	359.3
Maximum	786
Range	786
Interquartile range (IQR)	140

Descriptive statistics

Standard deviation	118.48606
Coefficient of variation (CV)	0.87178194
Kurtosis	3.4806257
Mean	135.9125
Median Absolute Deviation (MAD)	65
Skewness	1.5954133
Sum	195714
Variance	14038.947
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	25	1.7%
2	19	1.3%
46	12	0.8%
6	12	0.8%
40	11	0.8%
56	11	0.8%
16	11	0.8%
12	11	0.8%
100	11	0.8%
18	10	0.7%
Other values (371)	1307	90.8%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	25	1.7%
2	19	1.3%
3	5	0.3%
4	8	0.6%
5	3	0.2%
6	12	0.8%
7	5	0.3%
8	10	0.7%
9	2	0.1%
10	6	0.4%

Value	Count	Frequency (%)
786	1	0.1%
760	1	0.1%
745	1	0.1%
686	1	0.1%
673	1	0.1%
654	1	0.1%
623	1	0.1%
622	1	0.1%
614	1	0.1%
599	1	0.1%

vehicleClass_others
Real number (ℝ)

Distinct	578
Distinct (%)	40.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	339.32778

Minimum	0
Maximum	4299
Zeros	1
Zeros (%)	0.1%
Negative	0
Negative (%)	0.0%
Memory size	22.5 KiB

Quantile statistics

Minimum	0
5-th percentile	60.95
Q1	122
median	199
Q3	332.25
95-th percentile	1322.2
Maximum	4299
Range	4299
Interquartile range (IQR)	210.25

Descriptive statistics

Standard deviation	449.46776
Coefficient of variation (CV)	1.3245829
Kurtosis	16.86754
Mean	339.32778
Median Absolute Deviation (MAD)	95
Skewness	3.6465957
Sum	488632
Variance	202021.27
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
86	11	0.8%
147	10	0.7%
178	9	0.6%
152	9	0.6%
177	9	0.6%
160	9	0.6%
144	8	0.6%
161	8	0.6%
74	8	0.6%
205	8	0.6%
Other values (568)	1351	93.8%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	1	0.1%
1	1	0.1%
2	1	0.1%
6	2	0.1%
7	3	0.2%
8	1	0.1%
9	2	0.1%
10	1	0.1%
12	4	0.3%
14	3	0.2%

Value	Count	Frequency (%)
4299	1	0.1%
3749	1	0.1%
3642	1	0.1%
3276	1	0.1%
3272	1	0.1%
3116	1	0.1%
2647	1	0.1%
2590	1	0.1%
2530	1	0.1%
2443	1	0.1%

seatCapacity_1_to_3
Real number (ℝ)

Distinct	1255
Distinct (%)	87.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3750.5167

Minimum	6
Maximum	36694
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	22.5 KiB

Quantile statistics

Minimum	6
5-th percentile	670.25
Q1	1486.25
median	2244.5
Q3	3635.5
95-th percentile	16261.2
Maximum	36694
Range	36688
Interquartile range (IQR)	2149.25

Descriptive statistics

Standard deviation	4609.4072
Coefficient of variation (CV)	1.2290059
Kurtosis	8.6603665
Mean	3750.5167
Median Absolute Deviation (MAD)	963.5
Skewness	2.8827111
Sum	5400744
Variance	21246635
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1065	4	0.3%
1318	4	0.3%
3827	4	0.3%
868	4	0.3%
1656	3	0.2%
3385	3	0.2%
1789	3	0.2%
1596	3	0.2%
1388	3	0.2%
1516	3	0.2%
Other values (1245)	1406	97.6%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
6	1	0.1%
15	1	0.1%
18	1	0.1%
19	1	0.1%
23	1	0.1%
25	1	0.1%
32	2	0.1%
34	1	0.1%
36	2	0.1%
38	1	0.1%

Value	Count	Frequency (%)
36694	1	0.1%
29579	1	0.1%
28023	1	0.1%
27197	1	0.1%
25359	1	0.1%
25250	1	0.1%
25215	1	0.1%
23539	1	0.1%
23524	1	0.1%
23513	1	0.1%

seatCapacity_4_to_6
Real number (ℝ)

HIGH CORRELATION ZEROS

Distinct	735
Distinct (%)	51.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	768.71528

Minimum	0
Maximum	7918
Zeros	25
Zeros (%)	1.7%
Negative	0
Negative (%)	0.0%
Memory size	22.5 KiB

Quantile statistics

Minimum	0
5-th percentile	68
Q1	154
median	273
Q3	543.5
95-th percentile	4706.25
Maximum	7918
Range	7918
Interquartile range (IQR)	389.5

Descriptive statistics

Standard deviation	1413.9333
Coefficient of variation (CV)	1.8393459
Kurtosis	7.9570332
Mean	768.71528
Median Absolute Deviation (MAD)	145
Skewness	2.9600651
Sum	1106950
Variance	1999207.4
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	25	1.7%
145	12	0.8%
179	9	0.6%
204	9	0.6%
116	9	0.6%
144	8	0.6%
142	8	0.6%
245	7	0.5%
220	7	0.5%
114	7	0.5%
Other values (725)	1339	93.0%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	25	1.7%
1	1	0.1%
2	4	0.3%
21	1	0.1%
26	3	0.2%
28	1	0.1%
30	2	0.1%
33	1	0.1%
42	1	0.1%
43	1	0.1%

Value	Count	Frequency (%)
7918	1	0.1%
7568	1	0.1%
7496	1	0.1%
7434	1	0.1%
7325	1	0.1%
7201	1	0.1%
7158	1	0.1%
7139	1	0.1%
7032	1	0.1%
6983	1	0.1%

seatCapacity_above_6
Real number (ℝ)

HIGH CORRELATION ZEROS

Distinct	290
Distinct (%)	20.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	108.19028

Minimum	0
Maximum	1337
Zeros	28
Zeros (%)	1.9%
Negative	0
Negative (%)	0.0%
Memory size	22.5 KiB

Quantile statistics

Minimum	0
5-th percentile	7
Q1	20
median	38
Q3	75.25
95-th percentile	682.1
Maximum	1337
Range	1337
Interquartile range (IQR)	55.25

Descriptive statistics

Standard deviation	209.77188
Coefficient of variation (CV)	1.9389162
Kurtosis	9.8343478
Mean	108.19028
Median Absolute Deviation (MAD)	22
Skewness	3.1828708
Sum	155794
Variance	44004.242
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
20	37	2.6%
18	33	2.3%
22	32	2.2%
14	32	2.2%
8	30	2.1%
0	28	1.9%
32	27	1.9%
24	26	1.8%
16	24	1.7%
11	24	1.7%
Other values (280)	1147	79.7%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	28	1.9%
1	2	0.1%
2	6	0.4%
3	3	0.2%
4	8	0.6%
5	8	0.6%
6	16	1.1%
7	9	0.6%
8	30	2.1%
9	12	0.8%

Value	Count	Frequency (%)
1337	1	0.1%
1237	1	0.1%
1232	1	0.1%
1221	1	0.1%
1220	1	0.1%
1217	1	0.1%
1120	1	0.1%
1111	1	0.1%
1093	1	0.1%
1085	1	0.1%

brand_new_vehicles
Real number (ℝ)

Distinct	1276
Distinct (%)	88.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4317.7785

Minimum	6
Maximum	42073
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	22.5 KiB

Quantile statistics

Minimum	6
5-th percentile	762.8
Q1	1629.75
median	2457
Q3	4095.25
95-th percentile	19700.2
Maximum	42073
Range	42067
Interquartile range (IQR)	2465.5

Descriptive statistics

Standard deviation	5531.4115
Coefficient of variation (CV)	1.2810781
Kurtosis	8.0067854
Mean	4317.7785
Median Absolute Deviation (MAD)	1072
Skewness	2.8330494
Sum	6217601
Variance	30596514
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1481	4	0.3%
2903	4	0.3%
1992	3	0.2%
2217	3	0.2%
2545	3	0.2%
2581	3	0.2%
3230	3	0.2%
1194	3	0.2%
3276	3	0.2%
2361	3	0.2%
Other values (1266)	1408	97.8%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
6	1	0.1%
15	1	0.1%
18	1	0.1%
19	1	0.1%
23	1	0.1%
25	1	0.1%
30	1	0.1%
34	2	0.1%
36	2	0.1%
38	1	0.1%

Value	Count	Frequency (%)
42073	1	0.1%
35033	1	0.1%
33786	1	0.1%
31319	1	0.1%
29401	1	0.1%
29328	1	0.1%
28828	1	0.1%
27738	1	0.1%
27121	1	0.1%
26560	1	0.1%

pre_owned_vehicles
Real number (ℝ)

HIGH CORRELATION ZEROS

Distinct	428
Distinct (%)	29.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	311.34306

Minimum	0
Maximum	4450
Zeros	24
Zeros (%)	1.7%
Negative	0
Negative (%)	0.0%
Memory size	22.5 KiB

Quantile statistics

Minimum	0
5-th percentile	25
Q1	57
median	101
Q3	167
95-th percentile	2097.15
Maximum	4450
Range	4450
Interquartile range (IQR)	110

Descriptive statistics

Standard deviation	648.26039
Coefficient of variation (CV)	2.0821418
Kurtosis	9.7209792
Mean	311.34306
Median Absolute Deviation (MAD)	49
Skewness	3.1739966
Sum	448334
Variance	420241.53
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	24	1.7%
52	17	1.2%
88	17	1.2%
66	16	1.1%
46	15	1.0%
57	15	1.0%
58	14	1.0%
72	14	1.0%
38	14	1.0%
49	14	1.0%
Other values (418)	1280	88.9%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	24	1.7%
2	5	0.3%
4	2	0.1%
8	1	0.1%
10	2	0.1%
12	3	0.2%
13	1	0.1%
14	2	0.1%
15	4	0.3%
16	3	0.2%

Value	Count	Frequency (%)
4450	1	0.1%
4193	1	0.1%
3912	1	0.1%
3750	1	0.1%
3726	1	0.1%
3701	1	0.1%
3593	1	0.1%
3404	1	0.1%
3320	1	0.1%
3265	1	0.1%

category_non_transport
Real number (ℝ)

Distinct	1243
Distinct (%)	86.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4213.1049

Minimum	6
Maximum	42074
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	22.5 KiB

Quantile statistics

Minimum	6
5-th percentile	700.25
Q1	1497
median	2295
Q3	3783.25
95-th percentile	20101.75
Maximum	42074
Range	42068
Interquartile range (IQR)	2286.25

Descriptive statistics

Standard deviation	5693.8004
Coefficient of variation (CV)	1.35145
Kurtosis	7.7181745
Mean	4213.1049
Median Absolute Deviation (MAD)	1004
Skewness	2.8321689
Sum	6066871
Variance	32419363
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1746	4	0.3%
1167	4	0.3%
1043	4	0.3%
1733	4	0.3%
1044	3	0.2%
2005	3	0.2%
1937	3	0.2%
1363	3	0.2%
37	3	0.2%
1431	3	0.2%
Other values (1233)	1406	97.6%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
6	1	0.1%
10	1	0.1%
12	1	0.1%
13	1	0.1%
14	1	0.1%
16	1	0.1%
18	2	0.1%
22	1	0.1%
24	1	0.1%
27	1	0.1%

Value	Count	Frequency (%)
42074	1	0.1%
35218	1	0.1%
33305	1	0.1%
29698	1	0.1%
28769	1	0.1%
28247	1	0.1%
27989	1	0.1%
27976	1	0.1%
27809	1	0.1%
27421	1	0.1%

category_transport
Real number (ℝ)

Distinct	680
Distinct (%)	47.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	416.01667

Minimum	0
Maximum	4239
Zeros	2
Zeros (%)	0.1%
Negative	0
Negative (%)	0.0%
Memory size	22.5 KiB

Quantile statistics

Minimum	0
5-th percentile	71.9
Q1	149
median	250
Q3	449.25
95-th percentile	1409.5
Maximum	4239
Range	4239
Interquartile range (IQR)	300.25

Descriptive statistics

Standard deviation	525.92418
Coefficient of variation (CV)	1.2641902
Kurtosis	15.150698
Mean	416.01667
Median Absolute Deviation (MAD)	125
Skewness	3.5218773
Sum	599064
Variance	276596.24
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
157	10	0.7%
120	10	0.7%
197	9	0.6%
225	8	0.6%
107	8	0.6%
284	8	0.6%
180	7	0.5%
168	7	0.5%
84	7	0.5%
203	7	0.5%
Other values (670)	1359	94.4%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	2	0.1%
1	1	0.1%
4	2	0.1%
5	1	0.1%
7	2	0.1%
8	2	0.1%
9	2	0.1%
10	3	0.2%
12	3	0.2%
13	1	0.1%

Value	Count	Frequency (%)
4239	1	0.1%
3844	1	0.1%
3811	1	0.1%
3804	1	0.1%
3788	1	0.1%
3666	1	0.1%
3565	1	0.1%
3441	1	0.1%
3403	1	0.1%
3314	1	0.1%

district
Categorical

Distinct	30
Distinct (%)	2.1%
Missing	0
Missing (%)	0.0%
Memory size	22.5 KiB

Rangareddy	48
Kamareddy	48
Rajanna Sircilla	48
Jangoan	48
Jayashankar Bhupalpally	48
Other values (25)	1200

Length

Max length	23
Median length	19.5
Mean length	11.2
Min length	5

Characters and Unicode

Total characters	16128
Distinct characters	40
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Rangareddy
2nd row	Kamareddy
3rd row	Rajanna Sircilla
4th row	Jangoan
5th row	Jayashankar Bhupalpally

Common Values

Value	Count	Frequency (%)
Rangareddy	48	3.3%
Kamareddy	48	3.3%
Rajanna Sircilla	48	3.3%
Jangoan	48	3.3%
Jayashankar Bhupalpally	48	3.3%
Mahabubnagar	48	3.3%
Siddipet	48	3.3%
Nagarkurnool	48	3.3%
Nirmal	48	3.3%
Jogulamba Gadwal	48	3.3%
Other values (20)	960	66.7%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
rangareddy	48	2.8%
kamareddy	48	2.8%
bhuvanagiri	48	2.8%
karimnagar	48	2.8%
hyderabad	48	2.8%
medchal_malkajgiri	48	2.8%
sangareddy	48	2.8%
medak	48	2.8%
nizamabad	48	2.8%
wanaparthy	48	2.8%
Other values (26)	1248	72.2%

Most occurring characters

Value	Count	Frequency (%)
a	3696	22.9%
d	1296	8.0%
r	1104	6.8%
i	912	5.7%
l	864	5.4%
n	720	4.5%
g	624	3.9%
e	624	3.9%
m	576	3.6%
b	576	3.6%
Other values (30)	5136	31.8%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	14016	86.9%
Uppercase Letter	1776	11.0%
Space Separator	288	1.8%
Connector Punctuation	48	0.3%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
a	3696	26.4%
d	1296	9.2%
r	1104	7.9%
i	912	6.5%
l	864	6.2%
n	720	5.1%
g	624	4.5%
e	624	4.5%
m	576	4.1%
b	576	4.1%
Other values (14)	3024	21.6%

Uppercase Letter

Value	Count	Frequency (%)
M	288	16.2%
K	240	13.5%
J	192	10.8%
N	192	10.8%
S	192	10.8%
B	144	8.1%
W	96	5.4%
A	96	5.4%
R	96	5.4%
G	48	2.7%
Other values (4)	192	10.8%

Space Separator

Value	Count	Frequency (%)
	288	100.0%

Connector Punctuation

Value	Count	Frequency (%)
_	48	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	15792	97.9%
Common	336	2.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
a	3696	23.4%
d	1296	8.2%
r	1104	7.0%
i	912	5.8%
l	864	5.5%
n	720	4.6%
g	624	4.0%
e	624	4.0%
m	576	3.6%
b	576	3.6%
Other values (28)	4800	30.4%

Common

Value	Count	Frequency (%)
	288	85.7%
_	48	14.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	16128	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
a	3696	22.9%
d	1296	8.0%
r	1104	6.8%
i	912	5.7%
l	864	5.4%
n	720	4.5%
g	624	3.9%
e	624	3.9%
m	576	3.6%
b	576	3.6%
Other values (30)	5136	31.8%

mmm
Categorical

HIGH CORRELATION UNIFORM

Distinct	12
Distinct (%)	0.8%
Missing	0
Missing (%)	0.0%
Memory size	22.5 KiB

Apr	120
May	120
Jun	120
Jul	120
Aug	120
Other values (7)	840

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	4320
Distinct characters	22
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Apr
2nd row	Apr
3rd row	Apr
4th row	Apr
5th row	Apr

Common Values

Value	Count	Frequency (%)
Apr	120	8.3%
May	120	8.3%
Jun	120	8.3%
Jul	120	8.3%
Aug	120	8.3%
Sep	120	8.3%
Oct	120	8.3%
Nov	120	8.3%
Dec	120	8.3%
Jan	120	8.3%
Other values (2)	240	16.7%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
apr	120	8.3%
may	120	8.3%
jun	120	8.3%
jul	120	8.3%
aug	120	8.3%
sep	120	8.3%
oct	120	8.3%
nov	120	8.3%
dec	120	8.3%
jan	120	8.3%
Other values (2)	240	16.7%

Most occurring characters

Value	Count	Frequency (%)
a	360	8.3%
J	360	8.3%
u	360	8.3%
e	360	8.3%
A	240	5.6%
r	240	5.6%
M	240	5.6%
n	240	5.6%
p	240	5.6%
c	240	5.6%
Other values (12)	1440	33.3%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	2880	66.7%
Uppercase Letter	1440	33.3%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
a	360	12.5%
u	360	12.5%
e	360	12.5%
r	240	8.3%
n	240	8.3%
p	240	8.3%
c	240	8.3%
v	120	4.2%
o	120	4.2%
t	120	4.2%
Other values (4)	480	16.7%

Uppercase Letter

Value	Count	Frequency (%)
J	360	25.0%
A	240	16.7%
M	240	16.7%
N	120	8.3%
F	120	8.3%
D	120	8.3%
S	120	8.3%
O	120	8.3%

Most occurring scripts

Value	Count	Frequency (%)
Latin	4320	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
a	360	8.3%
J	360	8.3%
u	360	8.3%
e	360	8.3%
A	240	5.6%
r	240	5.6%
M	240	5.6%
n	240	5.6%
p	240	5.6%
c	240	5.6%
Other values (12)	1440	33.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	4320	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
a	360	8.3%
J	360	8.3%
u	360	8.3%
e	360	8.3%
A	240	5.6%
r	240	5.6%
M	240	5.6%
n	240	5.6%
p	240	5.6%
c	240	5.6%
Other values (12)	1440	33.3%

quarter
Categorical

HIGH CORRELATION UNIFORM

Distinct	4
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	22.5 KiB

Q1	360
Q2	360
Q3	360
Q4	360

Length

Max length	2
Median length	2
Mean length	2
Min length	2

Characters and Unicode

Total characters	2880
Distinct characters	5
Distinct categories	2 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Q1
2nd row	Q1
3rd row	Q1
4th row	Q1
5th row	Q1

Common Values

Value	Count	Frequency (%)
Q1	360	25.0%
Q2	360	25.0%
Q3	360	25.0%
Q4	360	25.0%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
q1	360	25.0%
q2	360	25.0%
q3	360	25.0%
q4	360	25.0%

Most occurring characters

Value	Count	Frequency (%)
Q	1440	50.0%
1	360	12.5%
2	360	12.5%
3	360	12.5%
4	360	12.5%

Most occurring categories

Value	Count	Frequency (%)
Uppercase Letter	1440	50.0%
Decimal Number	1440	50.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
1	360	25.0%
2	360	25.0%
3	360	25.0%
4	360	25.0%

Uppercase Letter

Value	Count	Frequency (%)
Q	1440	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	1440	50.0%
Common	1440	50.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
1	360	25.0%
2	360	25.0%
3	360	25.0%
4	360	25.0%

Latin

Value	Count	Frequency (%)
Q	1440	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2880	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
Q	1440	50.0%
1	360	12.5%
2	360	12.5%
3	360	12.5%
4	360	12.5%

fiscal_year
Categorical

HIGH CORRELATION UNIFORM

Distinct	4
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	22.5 KiB

2019	360
2020	360
2021	360
2022	360

Length

Max length	4
Median length	4
Mean length	4
Min length	4

Characters and Unicode

Total characters	5760
Distinct characters	4
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	2019
2nd row	2019
3rd row	2019
4th row	2019
5th row	2019

Common Values

Value	Count	Frequency (%)
2019	360	25.0%
2020	360	25.0%
2021	360	25.0%
2022	360	25.0%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
2019	360	25.0%
2020	360	25.0%
2021	360	25.0%
2022	360	25.0%

Most occurring characters

Value	Count	Frequency (%)
2	2880	50.0%
0	1800	31.2%
1	720	12.5%
9	360	6.2%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	5760	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
2	2880	50.0%
0	1800	31.2%
1	720	12.5%
9	360	6.2%

Most occurring scripts

Value	Count	Frequency (%)
Common	5760	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
2	2880	50.0%
0	1800	31.2%
1	720	12.5%
9	360	6.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	5760	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
2	2880	50.0%
0	1800	31.2%
1	720	12.5%
9	360	6.2%

Auto

Heatmap
Table

	id	fuel_type_petrol	fuel_type_diesel	fuel_type_electric	fuel_type_others	vehicleClass_MotorCycle	vehicleClass_MotorCar	vehicleClass_AutoRickshaw	vehicleClass_Agriculture	vehicleClass_others	seatCapacity_1_to_3	seatCapacity_4_to_6	seatCapacity_above_6	brand_new_vehicles	pre_owned_vehicles	category_non_transport	category_transport	district	mmm	quarter	fiscal_year
id	1.000	-0.294	-0.274	0.620	0.406	-0.335	0.078	-0.196	-0.097	-0.135	-0.316	-0.039	0.022	-0.295	0.108	-0.283	-0.186	0.000	0.376	0.573	0.929
fuel_type_petrol	-0.294	1.000	0.863	0.264	0.388	0.997	0.836	0.295	0.230	0.794	0.993	0.851	0.785	0.994	0.813	0.997	0.781	0.402	0.046	0.065	0.123
fuel_type_diesel	-0.274	0.863	1.000	0.206	0.410	0.859	0.759	0.361	0.355	0.917	0.898	0.821	0.779	0.907	0.761	0.883	0.915	0.341	0.055	0.053	0.191
fuel_type_electric	0.620	0.264	0.206	1.000	0.664	0.233	0.549	-0.011	-0.236	0.288	0.234	0.482	0.494	0.261	0.552	0.271	0.277	0.214	0.041	0.052	0.140
fuel_type_others	0.406	0.388	0.410	0.664	1.000	0.358	0.623	0.272	-0.076	0.471	0.369	0.633	0.623	0.405	0.634	0.399	0.503	0.249	0.000	0.036	0.113
vehicleClass_MotorCycle	-0.335	0.997	0.859	0.233	0.358	1.000	0.799	0.301	0.223	0.781	0.994	0.823	0.759	0.991	0.786	0.994	0.773	0.430	0.032	0.037	0.136
vehicleClass_MotorCar	0.078	0.836	0.759	0.549	0.623	0.799	1.000	0.239	0.154	0.744	0.807	0.955	0.884	0.838	0.906	0.844	0.723	0.414	0.000	0.000	0.054
vehicleClass_AutoRickshaw	-0.196	0.295	0.361	-0.011	0.272	0.301	0.239	1.000	0.147	0.322	0.294	0.422	0.341	0.322	0.298	0.293	0.498	0.252	0.000	0.000	0.189
vehicleClass_Agriculture	-0.097	0.230	0.355	-0.236	-0.076	0.223	0.154	0.147	1.000	0.199	0.273	0.113	0.085	0.262	0.141	0.267	0.127	0.249	0.109	0.068	0.141
vehicleClass_others	-0.135	0.794	0.917	0.288	0.471	0.781	0.744	0.322	0.199	1.000	0.821	0.793	0.793	0.834	0.751	0.800	0.956	0.328	0.029	0.060	0.126
seatCapacity_1_to_3	-0.316	0.993	0.898	0.234	0.369	0.994	0.807	0.294	0.273	0.821	1.000	0.828	0.769	0.997	0.796	0.995	0.802	0.407	0.027	0.048	0.142
seatCapacity_4_to_6	-0.039	0.851	0.821	0.482	0.633	0.823	0.955	0.422	0.113	0.793	0.828	1.000	0.895	0.865	0.901	0.856	0.834	0.385	0.065	0.078	0.057
seatCapacity_above_6	0.022	0.785	0.779	0.494	0.623	0.759	0.884	0.341	0.085	0.793	0.769	0.895	1.000	0.800	0.863	0.791	0.803	0.359	0.072	0.048	0.115
brand_new_vehicles	-0.295	0.994	0.907	0.261	0.405	0.991	0.838	0.322	0.262	0.834	0.997	0.865	0.800	1.000	0.817	0.996	0.825	0.396	0.065	0.081	0.138
pre_owned_vehicles	0.108	0.813	0.761	0.552	0.634	0.786	0.906	0.298	0.141	0.751	0.796	0.901	0.863	0.817	1.000	0.821	0.748	0.381	0.024	0.052	0.098
category_non_transport	-0.283	0.997	0.883	0.271	0.399	0.994	0.844	0.293	0.267	0.800	0.995	0.856	0.791	0.996	0.821	1.000	0.785	0.398	0.053	0.050	0.108
category_transport	-0.186	0.781	0.915	0.277	0.503	0.773	0.723	0.498	0.127	0.956	0.802	0.834	0.803	0.825	0.748	0.785	1.000	0.331	0.032	0.070	0.182
district	0.000	0.402	0.341	0.214	0.249	0.430	0.414	0.252	0.249	0.328	0.407	0.385	0.359	0.396	0.381	0.398	0.331	1.000	0.000	0.000	0.000
mmm	0.376	0.046	0.055	0.041	0.000	0.032	0.000	0.000	0.109	0.029	0.027	0.065	0.072	0.065	0.024	0.053	0.032	0.000	1.000	0.997	0.000
quarter	0.573	0.065	0.053	0.052	0.036	0.037	0.000	0.000	0.068	0.060	0.048	0.078	0.048	0.081	0.052	0.050	0.070	0.000	0.997	1.000	0.000
fiscal_year	0.929	0.123	0.191	0.140	0.113	0.136	0.054	0.189	0.141	0.126	0.142	0.057	0.115	0.138	0.098	0.108	0.182	0.000	0.000	0.000	1.000

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows
Last rows

	id	month	fuel_type_petrol	fuel_type_diesel	fuel_type_electric	fuel_type_others	vehicleClass_MotorCycle	vehicleClass_MotorCar	vehicleClass_AutoRickshaw	vehicleClass_Agriculture	vehicleClass_others	seatCapacity_1_to_3	seatCapacity_4_to_6	seatCapacity_above_6	brand_new_vehicles	pre_owned_vehicles	category_non_transport	category_transport	district	mmm	quarter	fiscal_year
0	1	2019-04-01	17910	3011	76	22	15308	4429	0	4	1278	16110	4182	717	19542	1477	19856	1163	Rangareddy	Apr	Q1	2019
1	2	2019-04-01	3066	306	6	0	2995	142	49	64	128	3156	189	33	3322	56	3203	175	Kamareddy	Apr	Q1	2019
2	3	2019-04-01	1577	215	0	0	1546	79	29	21	117	1683	104	5	1751	41	1648	144	Rajanna Sircilla	Apr	Q1	2019
3	4	2019-04-01	1961	281	2	0	1939	72	72	48	113	2082	146	16	2209	35	2075	169	Jangoan	Apr	Q1	2019
4	5	2019-04-01	1552	309	0	0	1512	76	69	109	95	1696	145	20	1820	41	1701	160	Jayashankar Bhupalpally	Apr	Q1	2019
5	6	2019-04-01	4550	660	0	0	4431	264	193	84	238	4714	450	44	5104	106	4802	408	Mahabubnagar	Apr	Q1	2019
6	7	2019-04-01	3112	496	0	0	3038	233	70	72	195	3276	279	53	3470	138	3353	255	Siddipet	Apr	Q1	2019
7	8	2019-04-01	2200	496	0	0	2160	110	74	162	190	2483	194	19	2645	51	2440	256	Nagarkurnool	Apr	Q1	2019
8	9	2019-04-01	1736	263	2	9	1678	121	55	70	86	1806	173	31	1980	30	1879	131	Nirmal	Apr	Q1	2019
9	10	2019-04-01	2033	173	0	0	2017	67	22	47	53	2111	87	8	2162	44	2134	72	Jogulamba Gadwal	Apr	Q1	2019

	id	month	fuel_type_petrol	fuel_type_diesel	fuel_type_electric	fuel_type_others	vehicleClass_MotorCycle	vehicleClass_MotorCar	vehicleClass_AutoRickshaw	vehicleClass_Agriculture	vehicleClass_others	seatCapacity_1_to_3	seatCapacity_4_to_6	seatCapacity_above_6	brand_new_vehicles	pre_owned_vehicles	category_non_transport	category_transport	district	mmm	quarter	fiscal_year
1430	1431	2023-03-01	1154	243	4	4	1105	61	56	34	149	1282	108	15	1348	57	1202	203	Kumurambheem Asifabad	Mar	Q4	2022
1431	1432	2023-03-01	3044	444	316	42	2791	624	136	99	196	3032	722	92	3582	264	3526	320	Nizamabad	Mar	Q4	2022
1432	1433	2023-03-01	1455	280	48	97	1255	277	112	75	161	1451	402	27	1758	122	1615	265	Medak	Mar	Q4	2022
1433	1434	2023-03-01	5295	1004	491	679	4279	1677	667	124	724	4883	2248	322	6648	823	6110	1361	Sangareddy	Mar	Q4	2022
1434	1435	2023-03-01	20794	2944	2144	302	17079	6724	31	23	2327	18622	6427	1051	22864	3320	23896	2288	Medchal_Malkajgiri	Mar	Q4	2022
1435	1436	2023-03-01	24112	2169	2606	1848	21501	5792	1684	0	1758	22493	7139	1093	26285	4450	27421	3314	Hyderabad	Mar	Q4	2022
1436	1437	2023-03-01	996	286	20	67	884	137	75	106	167	1123	219	27	1300	69	1129	240	Wanaparthy	Mar	Q4	2022
1437	1438	2023-03-01	886	183	71	12	828	146	17	54	107	966	167	19	1060	92	1030	122	Rajanna Sircilla	Mar	Q4	2022
1438	1439	2023-03-01	1659	472	39	97	1413	289	95	182	288	1829	391	47	2107	160	1889	378	Yadadri Bhuvanagiri	Mar	Q4	2022
1439	1440	2023-03-01	1767	239	32	28	1532	251	65	58	160	1680	344	42	1954	112	1845	221	Kamareddy	Mar	Q4	2022

Overview

Variables

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Space Separator

Connector Punctuation

Most occurring scripts

Most frequent character per script

Latin

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Most occurring scripts

Most frequent character per script

Latin

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Uppercase Letter

Most occurring scripts

Most frequent character per script

Common

Latin

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Interactions

Correlations

Missing values

Sample